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Microsoft AI-103 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Developing AI Apps and Agents on Azure AI
Exam Number:AI-103
Related Certifications:Microsoft Certified: Azure Developer Associate
Microsoft Certified: Azure AI Engineer Associate
Microsoft Certified: Azure AI Fundamentals
Exam Price:USD 165 (may vary by region)
Passing Score:700/1000
Exam Format:Scenario-based questions, Drag and drop, Multiple response, Case studies, Multiple choice
Available Languages:English
Exam Duration:120-180
Certificate Validity Period:1 year (renewable via Microsoft Learn assessment)
Real Exam Qty:40-60
Recommended Training:Microsoft Learn - Azure AI Engineer Learning Path
Azure OpenAI Service Documentation
Exam Registration:Microsoft Certification Dashboard
Pearson VUE Microsoft Exams
Sample Questions:Microsoft AI-103 Sample Questions
Exam Way:Online proctored exam or authorized test center
Pre Condition:Recommended: experience with Azure services, Python or C#, and basic machine learning concepts. Prior knowledge of Azure AI-102 or equivalent is helpful but not mandatory.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/

Microsoft AI-103 Exam Syllabus Topics:

SectionObjectives
Topic 1: Implement Computer Vision Solutions- OCR and document intelligence
- Image classification and object detection
Topic 2: Knowledge Mining and Information Retrieval- Azure AI Search configuration
- Indexing and semantic search
- RAG (Retrieval Augmented Generation) patterns
Topic 3: Implement Natural Language Processing Solutions- Translation and multilingual support
- Text analytics and summarization
- Language understanding and intent recognition
Topic 4: Develop Generative AI Applications and Agents- AI agents architecture
  • 1. Agent orchestration and workflows
    • 2. Memory and state management
      - Azure OpenAI Service integration
      • 1. Prompt engineering and prompt flow design
        • 2. Function calling and tool use
          Topic 5: Plan and Manage Azure AI Solutions- Model selection and lifecycle management
          - Responsible AI principles and governance
          - Azure AI resource provisioning and configuration

          Microsoft Developing AI Apps and Agents on Azure Sample Questions:

          Question #1

          Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
          After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
          You have a multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
          You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
          You need to implement controls to mitigate the risk.
          Solution: You configure image moderation to block unsafe content before processing the images.
          Does this meet the goal?

          • A. Yes
          • B. No
          Reveal Solution  Discussion  0

          Correct Answer: A  🗳️

          Explanation: Only visible for Exams-boost members. You can sign-up / login (it's free).

          Question #2

          Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
          After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
          You have a multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
          You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
          You need to implement controls to mitigate the risk.
          Solution: You configure protected material detection.
          Does this meet the goal?

          • A. Yes
          • B. No
          Reveal Solution  Discussion  0

          Correct Answer: B  🗳️

          Explanation: Only visible for Exams-boost members. You can sign-up / login (it's free).

          Question #3

          Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
          After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
          You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
          Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
          You need to improve response completeness.
          Solution: You increase the value of the temperatureparameter.
          Does this meet the goal?

          • A. Yes
          • B. No
          Reveal Solution  Discussion  0

          Correct Answer: B  🗳️

          Explanation: Only visible for Exams-boost members. You can sign-up / login (it's free).

          Question #4

          Case Study 1 - Contoso, Ltd
          Overview
          Company Information
          Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.
          Existing Environment
          Identity Environment
          Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
          Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.
          The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.
          Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.
          Generative Environment
          Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
          Project1
          Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
          Agent1 has the following configurations:
          - Agent1 uses a base model deployment.
          - A safety evaluation pipeline is NOT enabled.
          - Tool invocation approval workflows are NOT enabled.
          - Conversation memory constraints are NOT configured.
          Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
          Project1 is deployed to an Azure region located in the European Union (EU).
          Agent1Dev Team will use Project1 to optimize and maintain Agent1.
          Project2
          Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
          Development of the solution is incomplete.
          Data Environment
          Contoso stores product-related information in Azure resources that support AI applications.
          The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
          The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
          Problem Statements
          Contoso identifies the following issues:
          - Agent1 has only general knowledge of the Contoso products.
          - A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
          - Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
          - The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
          Requirements
          Planned Changes
          Contoso plans to implement the following changes:
          - Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
          - Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
          - Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
          - Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
          - Complete the development of the video creation solution.
          Technical Requirements
          Contoso identifies the following technical requirements:
          - The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
          - The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
          - Responses generated by using the product sheet information must be relevant, complete, and accurate.
          - Agent1 must be able to use the product sheets to answer natural language questions about product details.
          - The model version used by Agent1 must remain consistent to ensure stable responses.
          - The data processed by the model must remain within the EU.
          Security and Compliance Requirements
          Contoso identifies the following security and compliance requirements:
          - API keys must NOT be used to access Foundry-deployed models.
          - Access to the Azure resources must follow the principle of least privilege.
          - The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
          - Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
          - Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
          - Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
          - The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
          Business Requirements
          Contoso identifies the following business requirements:
          - Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
          - Agent1 must answer questions only about the products sold by Contoso.
          Hotspot Question
          You need to configure the model deployment for Agent1 to meet the technical requirements.
          What should you configure? To answer, select the appropriate options in the answer area.
          NOTE: Each correct selection is worth one point.

          Reveal Solution  Discussion  0

          Correct Answer:

          Question #5

          You are building a voice agent for a pharmacy. It must accept spoken questions, reply with synthesised speech, and accurately recognise specialised medicine names that a general speech model often mishears. Which capability should you configure?

          • A. Azure AI Document Intelligence on the audio stream
          • B. Only the default text-to-speech voice
          • C. A custom speech model for speech-to-text within the agent
          • D. Azure Translator to convert the audio
          Reveal Solution  Discussion  0

          Correct Answer: C  🗳️

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